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acute myeloid leukemia (AML). They described
a process in which CD34
network are more likely to be associated with
the same or similar diseases. The
cells are treated
in vitro with a chemotherapeutic agent, and
based on the response of the cells, the patients
are categorized into either (1) normal
responders, (2) chemoresistant, or (3) highly
chemosensitive. From these cells, the expression
level of 84 genes associated with apoptotic
machinery (AM) were measured through
RT-PCR. They acquired protein interaction
data from the APID2NET 33 and analyzed
several network parameters such as
first step in
this process was to identify human disease genes
and their associated diseases from the Online
Mendelian Inheritance in Man (OMIM) data-
base. The authors then classi
þ
ed the genes into
either phenotypically similar (e.g., TYRP1 is
associated with brown and rufous albinism) or
phenotypically divergent (e.g., AKT1 is involved
with different diseases such as breast cancer,
ovarian cancer, colorectal cancer, and schizo-
phrenia) diseases based on CIPHER (Correlating
protein Interaction network and PHEnotype
network to pRedict disease genes) scores. 37
Through analysis of the interaction network,
they were able to determine that genes that are
shared among different diseases (phenotypically
divergent) are more central than speci
centrality
metrics
to determine whether proteins whose
expression levels deviated from the normal
had any unique properties that might account
for the clinical outcome. The term
centrality
metric
is related to the probability of a protein
being functionally relevant for other proteins
and its ability to connect different nodes within
the network. They found that in chemoresistant
patients there was a signi
c genes
that are related to only a single disease or pheno-
typically similar genes. Second, genes involved
in different diseases had a higher number of
interacting proteins comparing to speci
cant upregulation of
the anti-apoptotic BIRC genes and down regula-
tion of pro-apoptotic genes. They found that
some critical nodes of the AM network were
different between the normal responders and
the chemoresistant groups, which could repre-
sent one of the reasons for the differential resis-
tance to chemotherapy. Thus AM pro
c genes
but also had a more restrictive coexpression
pro
le as related to interactors that manifests
its role in different diseases. Last, when essential
genes (deletion of which cause lethality) were
compared to speci
c, similar, and divergent
genes, it was determined that divergent and
essential genes have a similar level of centrality
that was higher than speci
ling of
the patients would allow the clinicians to iden-
tify the patients with a genotype highly predis-
posing to relapse and help them to
c or similar genes.
Also, both essential and shared genes act as intra-
and intermodular hubs but essential genes are
more likely to be coexpressed with their interact-
ing partners. This study demonstrates that
network properties of the gene dictate the pheno-
typic disease diversity and that understanding of
these networks would help to better predict
biomarkers and develop novel therapeutics.
Even though these three studies differ in their
approaches, with some attempting to identify
the network properties of typical biomarkers
with descriptors
nd an
alternative regiment of drugs.
In contrast, Benson
s group 34 took a different
approach in the use of network data in an
attempt to understand how different mutations
within a single gene can cause different patho-
logical effects. The primary hypothesis was that
network properties of a gene in a molecular
interaction network,
'
in conjunction with the
tissue expression pro
le of its interactors, are
the key determinants that drive different disease
phenotypes. This hypothesis is based on the
rationale, supported in studies performed by
Aerts et al. 35 and Franke et al., 36 that interacting
partners of a disease gene in an interaction
such as
connectivity and
and others looked for expression
patterns based on protein function or complex
formation, they possess a common theme in
betweenness
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